1. Technical Field
The present teaching relates to methods, systems and programming for identifying information based on content. Particularly, the present teaching is directed to methods, systems, and programming for providing explanations for relationships.
2. Discussion of Technical Background
The advancement in the world of the Internet has made it possible to make a tremendous amount of information accessible to users located anywhere in the world. With the explosion of information, new issues have arisen. First, much effort has been put in organizing the vast amount of information to facilitate the search for information in a more effective and systematic manner. Along that line, different techniques have been developed to automatically or semi-automatically categorize content on the internet into different topics and organize them in an, e.g., hierarchical fashion. Imposing organization and structure on content has led to more meaningful search and promoted more targeted commercial activities. For example, categorizing a piece of content into a class with a designated topic or interest often greatly facilitates the selection of advertisement information that is more on the point and relevant.
Another important issue has to do with how to identify useful information out of massive amounts of available content in order to link different pieces of information in a more meaningful manner. For example, effort has been spent towards identifying relationships among different entities, whether individuals or business organizations, as well as events that give rise to various relationships among such entities. To achieve that, content can be analyzed and various types of information can be abstracted through such analysis. Such identified relationships are usually individual relationships. In addition, existing approaches to detecting relationships merely provide a list of entities who are considered to be related to an entity in question. Although helpful, it is often a mystery to a viewer as to why and how particular two entities are related.
In addition, the same pairs of entities may be related in different ways, e.g., the same people may be related in different capacities. For instance, Brad Pitt and Angelina Jolie are related both as domestic partners privately and as co-starring actors professionally. Conventional approaches focus only on identifying individual relationships without providing any indication as to in how many different ways are two entities are related. Furthermore, each relationship, e.g., co-worker, is an abstracted concept, which does not provide, in and of itself, any detailed or real life information that can be used to explain each particular instances of the relationship. Hence, existing solutions to relationship detection, although useful in certain situations/applications, do not address the issue of providing explanations as to the nature of a given relationship or how given entities are related in real life in a multi-faceted way. Therefore, there is a need to develop techniques to create concrete explanations for suggested relationships existing among different entities based on accessible information.